Overview

Dataset statistics

Number of variables14
Number of observations7998
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory874.9 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric13

Variable descriptions

dateErfassungszeitpunkt
co_gtstündlich gemittelte CO-Konzentration
pt08_s1_costündlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)

Alerts

date has a high cardinality: 7998 distinct values High cardinality
co_gt is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 5 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 6 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 6 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 3 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 5 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 4 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with pt08_s1_co and 5 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 7 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt is highly correlated with nox_gt and 1 other fieldsHigh correlation
pt08_s1_co is highly correlated with c6h6_gt and 6 other fieldsHigh correlation
c6h6_gt is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 1 other fieldsHigh correlation
pt08_s3_nox is highly correlated with t and 2 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 1 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with pt08_s1_co and 5 other fieldsHigh correlation
t is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
rh is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
ah is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
co_gt is highly correlated with nox_gt and 1 other fieldsHigh correlation
pt08_s1_co is highly correlated with c6h6_gt and 4 other fieldsHigh correlation
c6h6_gt is highly correlated with pt08_s1_co and 4 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with pt08_s1_co and 4 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 1 other fieldsHigh correlation
pt08_s3_nox is highly correlated with pt08_s1_co and 3 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 1 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with pt08_s1_co and 2 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with pt08_s1_co and 3 other fieldsHigh correlation
t is highly correlated with ahHigh correlation
ah is highly correlated with tHigh correlation
pt08_s1_co is highly correlated with nmhc_gt and 7 other fieldsHigh correlation
nmhc_gt is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
c6h6_gt is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
nox_gt is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
pt08_s3_nox is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
no2_gt is highly correlated with pt08_s1_co and 5 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with pt08_s1_co and 6 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2High correlation
date is uniformly distributed Uniform
date has unique values Unique

Reproduction

Analysis started2022-04-29 12:01:30.578322
Analysis finished2022-04-29 12:01:48.212115
Duration17.63 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Erfassungszeitpunkt

Distinct7998
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
2004-03-10 18:00:00
 
1
2004-10-18 17:00:00
 
1
2004-10-19 06:00:00
 
1
2004-10-19 05:00:00
 
1
2004-10-19 04:00:00
 
1
Other values (7993)
7993 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7998 ?
Unique (%)100.0%

Sample

1st row2004-03-10 18:00:00
2nd row2004-03-10 19:00:00
3rd row2004-03-10 20:00:00
4th row2004-03-10 21:00:00
5th row2004-03-10 22:00:00

Common Values

ValueCountFrequency (%)
2004-03-10 18:00:001
 
< 0.1%
2004-10-18 17:00:001
 
< 0.1%
2004-10-19 06:00:001
 
< 0.1%
2004-10-19 05:00:001
 
< 0.1%
2004-10-19 04:00:001
 
< 0.1%
2004-10-19 03:00:001
 
< 0.1%
2004-10-19 02:00:001
 
< 0.1%
2004-10-19 01:00:001
 
< 0.1%
2004-10-19 00:00:001
 
< 0.1%
2004-10-18 23:00:001
 
< 0.1%
Other values (7988)7988
99.9%

Length

2022-04-29T14:01:48.284440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19:00:00334
 
2.1%
20:00:00334
 
2.1%
18:00:00334
 
2.1%
23:00:00334
 
2.1%
22:00:00334
 
2.1%
21:00:00334
 
2.1%
07:00:00333
 
2.1%
08:00:00333
 
2.1%
09:00:00333
 
2.1%
10:00:00333
 
2.1%
Other values (348)12660
79.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

co_gt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

stündlich gemittelte CO-Konzentration

Distinct96
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-39.62661915
Minimum-200
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative1654
Negative (%)20.7%
Memory size62.6 KiB
2022-04-29T14:01:48.364515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q10.5
median1.5
Q32.6
95-th percentile4.7
Maximum11.9
Range211.9
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation81.90333138
Coefficient of variation (CV)-2.066876588
Kurtosis0.09589990093
Mean-39.62661915
Median Absolute Deviation (MAD)1
Skewness-1.447160535
Sum-316933.7
Variance6708.155691
MonotonicityNot monotonic
2022-04-29T14:01:48.446441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2001654
 
20.7%
1240
 
3.0%
1.6227
 
2.8%
1.7221
 
2.8%
1.4215
 
2.7%
1.5215
 
2.7%
0.7214
 
2.7%
1.3212
 
2.7%
1.2209
 
2.6%
1.1203
 
2.5%
Other values (86)4388
54.9%
ValueCountFrequency (%)
-2001654
20.7%
0.125
 
0.3%
0.237
 
0.5%
0.387
 
1.1%
0.4134
 
1.7%
0.5181
 
2.3%
0.6197
 
2.5%
0.7214
 
2.7%
0.8197
 
2.5%
0.9192
 
2.4%
ValueCountFrequency (%)
11.91
< 0.1%
11.51
< 0.1%
10.22
< 0.1%
10.11
< 0.1%
9.91
< 0.1%
9.51
< 0.1%
9.41
< 0.1%
9.31
< 0.1%
9.21
< 0.1%
9.12
< 0.1%

pt08_s1_co
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

stündlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)

Distinct1028
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1051.390473
Minimum-200
Maximum2040
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:48.622511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile751.85
Q1917
median1051.5
Q31225
95-th percentile1506
Maximum2040
Range2240
Interquartile range (IQR)308

Descriptive statistics

Standard deviation324.5589983
Coefficient of variation (CV)0.3086950156
Kurtosis5.854740873
Mean1051.390473
Median Absolute Deviation (MAD)149.5
Skewness-1.650185639
Sum8409021
Variance105338.5434
MonotonicityNot monotonic
2022-04-29T14:01:48.704512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
96925
 
0.3%
97324
 
0.3%
110024
 
0.3%
92524
 
0.3%
89222
 
0.3%
96622
 
0.3%
96222
 
0.3%
105022
 
0.3%
105321
 
0.3%
Other values (1018)7503
93.8%
ValueCountFrequency (%)
-200289
3.6%
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
 
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%

nmhc_gt
Real number (ℝ)

HIGH CORRELATION

Distinct430
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-152.1387847
Minimum-200
Maximum1189
Zeros0
Zeros (%)0.0%
Negative7084
Negative (%)88.6%
Memory size62.6 KiB
2022-04-29T14:01:48.786512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q1-200
median-200
Q3-200
95-th percentile173
Maximum1189
Range1389
Interquartile range (IQR)0

Descriptive statistics

Standard deviation150.0967084
Coefficient of variation (CV)-0.9865775431
Kurtosis15.54328729
Mean-152.1387847
Median Absolute Deviation (MAD)0
Skewness3.716461271
Sum-1216806
Variance22529.02188
MonotonicityNot monotonic
2022-04-29T14:01:48.873520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2007084
88.6%
6614
 
0.2%
409
 
0.1%
299
 
0.1%
888
 
0.1%
938
 
0.1%
847
 
0.1%
557
 
0.1%
957
 
0.1%
607
 
0.1%
Other values (420)838
 
10.5%
ValueCountFrequency (%)
-2007084
88.6%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
 
< 0.1%
161
 
< 0.1%
174
 
0.1%
182
 
< 0.1%
ValueCountFrequency (%)
11891
< 0.1%
11291
< 0.1%
10841
< 0.1%
10421
< 0.1%
9741
< 0.1%
9261
< 0.1%
8991
< 0.1%
8801
< 0.1%
8721
< 0.1%
8401
< 0.1%

c6h6_gt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct406
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.853500875
Minimum-200
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:48.962523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile0.9
Q14.3
median8.3
Q314.2
95-th percentile24.9
Maximum63.7
Range263.7
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation39.97771659
Coefficient of variation (CV)14.01005934
Kurtosis20.99589177
Mean2.853500875
Median Absolute Deviation (MAD)4.6
Skewness-4.687309803
Sum22822.3
Variance1598.217824
MonotonicityNot monotonic
2022-04-29T14:01:49.049521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
368
 
0.9%
3.668
 
0.9%
2.867
 
0.8%
467
 
0.8%
3.865
 
0.8%
2.663
 
0.8%
5.462
 
0.8%
662
 
0.8%
3.161
 
0.8%
Other values (396)7126
89.1%
ValueCountFrequency (%)
-200289
3.6%
0.12
 
< 0.1%
0.25
 
0.1%
0.37
 
0.1%
0.413
 
0.2%
0.515
 
0.2%
0.617
 
0.2%
0.725
 
0.3%
0.818
 
0.2%
0.919
 
0.2%
ValueCountFrequency (%)
63.71
< 0.1%
52.11
< 0.1%
50.81
< 0.1%
50.71
< 0.1%
50.61
< 0.1%
49.51
< 0.1%
49.41
< 0.1%
48.21
< 0.1%
47.71
< 0.1%
47.51
< 0.1%

pt08_s2_nmhc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1222
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean911.895974
Minimum-200
Maximum2214
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:49.133441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile489.85
Q1727
median911
Q31122
95-th percentile1427.15
Maximum2214
Range2414
Interquartile range (IQR)395

Descriptive statistics

Standard deviation340.000627
Coefficient of variation (CV)0.3728502337
Kurtosis2.453179288
Mean911.895974
Median Absolute Deviation (MAD)197
Skewness-0.7866334169
Sum7293344
Variance115600.4263
MonotonicityNot monotonic
2022-04-29T14:01:49.218440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
85322
 
0.3%
77620
 
0.3%
85920
 
0.3%
81419
 
0.2%
88019
 
0.2%
76919
 
0.2%
85018
 
0.2%
96218
 
0.2%
90018
 
0.2%
Other values (1212)7536
94.2%
ValueCountFrequency (%)
-200289
3.6%
3832
 
< 0.1%
3881
 
< 0.1%
3901
 
< 0.1%
3971
 
< 0.1%
3991
 
< 0.1%
4021
 
< 0.1%
4071
 
< 0.1%
4101
 
< 0.1%
4123
 
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

nox_gt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct891
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.0965241
Minimum-200
Maximum1479
Zeros0
Zeros (%)0.0%
Negative1604
Negative (%)20.1%
Memory size62.6 KiB
2022-04-29T14:01:49.304443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q139
median122
Q3259.75
95-th percentile646
Maximum1479
Range1679
Interquartile range (IQR)220.75

Descriptive statistics

Standard deviation260.6283769
Coefficient of variation (CV)1.748051328
Kurtosis1.742978378
Mean149.0965241
Median Absolute Deviation (MAD)104.5
Skewness0.94132776
Sum1192474
Variance67927.15087
MonotonicityNot monotonic
2022-04-29T14:01:49.393442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2001604
 
20.1%
8939
 
0.5%
6537
 
0.5%
4136
 
0.5%
5732
 
0.4%
5132
 
0.4%
6131
 
0.4%
18031
 
0.4%
4631
 
0.4%
9331
 
0.4%
Other values (881)6094
76.2%
ValueCountFrequency (%)
-2001604
20.1%
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
 
< 0.1%
114
 
0.1%
124
 
0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13101
< 0.1%
13011
< 0.1%
12901
< 0.1%
12531
< 0.1%
12471
< 0.1%

pt08_s3_nox
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1204
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.8665916
Minimum-200
Maximum2683
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:49.487521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile429
Q1652
median807
Q3976
95-th percentile1301
Maximum2683
Range2883
Interquartile range (IQR)324

Descriptive statistics

Standard deviation321.3858069
Coefficient of variation (CV)0.3963485612
Kurtosis3.216668052
Mean810.8665916
Median Absolute Deviation (MAD)161
Skewness-0.3326971107
Sum6485311
Variance103288.8369
MonotonicityNot monotonic
2022-04-29T14:01:49.570440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
73324
 
0.3%
84624
 
0.3%
76722
 
0.3%
81621
 
0.3%
80021
 
0.3%
87620
 
0.3%
68520
 
0.3%
76520
 
0.3%
74819
 
0.2%
Other values (1194)7518
94.0%
ValueCountFrequency (%)
-200289
3.6%
3221
 
< 0.1%
3252
 
< 0.1%
3281
 
< 0.1%
3301
 
< 0.1%
3341
 
< 0.1%
3351
 
< 0.1%
3402
 
< 0.1%
3411
 
< 0.1%
3472
 
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23311
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20811
< 0.1%

no2_gt
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct268
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.15616404
Minimum-200
Maximum333
Zeros0
Zeros (%)0.0%
Negative1607
Negative (%)20.1%
Memory size62.6 KiB
2022-04-29T14:01:49.655510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q144
median90
Q3125
95-th percentile180.15
Maximum333
Range533
Interquartile range (IQR)81

Descriptive statistics

Standard deviation129.3307646
Coefficient of variation (CV)2.864077747
Kurtosis-0.1518844709
Mean45.15616404
Median Absolute Deviation (MAD)39
Skewness-1.124468508
Sum361159
Variance16726.44666
MonotonicityNot monotonic
2022-04-29T14:01:49.746517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2001607
 
20.1%
9772
 
0.9%
9569
 
0.9%
10167
 
0.8%
9666
 
0.8%
11465
 
0.8%
12165
 
0.8%
10764
 
0.8%
11964
 
0.8%
11064
 
0.8%
Other values (258)5795
72.5%
ValueCountFrequency (%)
-2001607
20.1%
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
3331
< 0.1%
3221
< 0.1%
3101
< 0.1%
3091
< 0.1%
3061
< 0.1%
3011
< 0.1%
2881
< 0.1%
2851
< 0.1%
2832
< 0.1%
2822
< 0.1%

pt08_s4_no2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1550
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1445.65929
Minimum-200
Maximum2775
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:49.831511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile807
Q11275.25
median1494
Q31698
95-th percentile2051.15
Maximum2775
Range2975
Interquartile range (IQR)422.75

Descriptive statistics

Standard deviation455.3477118
Coefficient of variation (CV)0.3149758142
Kurtosis4.15742293
Mean1445.65929
Median Absolute Deviation (MAD)211
Skewness-1.42815788
Sum11562383
Variance207341.5386
MonotonicityNot monotonic
2022-04-29T14:01:49.998440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
158022
 
0.3%
153920
 
0.3%
163819
 
0.2%
148819
 
0.2%
146719
 
0.2%
141818
 
0.2%
157017
 
0.2%
151117
 
0.2%
160416
 
0.2%
Other values (1540)7542
94.3%
ValueCountFrequency (%)
-200289
3.6%
6571
 
< 0.1%
6671
 
< 0.1%
6681
 
< 0.1%
6741
 
< 0.1%
6821
 
< 0.1%
6851
 
< 0.1%
6971
 
< 0.1%
6982
 
< 0.1%
7022
 
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26841
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26621
< 0.1%
26432
< 0.1%
26412
< 0.1%

pt08_s5_o3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1682
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean979.9647412
Minimum-200
Maximum2523
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:50.082442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile373
Q1710
median942
Q31253.75
95-th percentile1745.15
Maximum2523
Range2723
Interquartile range (IQR)543.75

Descriptive statistics

Standard deviation449.2001467
Coefficient of variation (CV)0.4583839885
Kurtosis0.7120722517
Mean979.9647412
Median Absolute Deviation (MAD)264.5
Skewness-0.009838203847
Sum7837758
Variance201780.7718
MonotonicityNot monotonic
2022-04-29T14:01:50.171451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
83619
 
0.2%
82518
 
0.2%
79917
 
0.2%
82617
 
0.2%
77717
 
0.2%
73715
 
0.2%
92615
 
0.2%
92314
 
0.2%
77914
 
0.2%
Other values (1672)7563
94.6%
ValueCountFrequency (%)
-200289
3.6%
2531
 
< 0.1%
2611
 
< 0.1%
2631
 
< 0.1%
2661
 
< 0.1%
2681
 
< 0.1%
2743
 
< 0.1%
2821
 
< 0.1%
2831
 
< 0.1%
2861
 
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%
24151
< 0.1%

t
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct420
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.54548637
Minimum-200
Maximum44.6
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:50.256511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile3.4
Q112.3
median18.7
Q325.1
95-th percentile35.1
Maximum44.6
Range244.6
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation41.83546511
Coefficient of variation (CV)3.623534234
Kurtosis20.65979406
Mean11.54548637
Median Absolute Deviation (MAD)6.4
Skewness-4.641463785
Sum92340.8
Variance1750.206141
MonotonicityNot monotonic
2022-04-29T14:01:50.344514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
20.855
 
0.7%
21.350
 
0.6%
20.247
 
0.6%
19.846
 
0.6%
23.743
 
0.5%
21.742
 
0.5%
13.842
 
0.5%
15.642
 
0.5%
14.641
 
0.5%
Other values (410)7301
91.3%
ValueCountFrequency (%)
-200289
3.6%
0.31
 
< 0.1%
0.61
 
< 0.1%
0.83
 
< 0.1%
13
 
< 0.1%
1.23
 
< 0.1%
1.34
 
0.1%
1.44
 
0.1%
1.52
 
< 0.1%
1.62
 
< 0.1%
ValueCountFrequency (%)
44.61
 
< 0.1%
44.31
 
< 0.1%
43.41
 
< 0.1%
43.11
 
< 0.1%
42.83
< 0.1%
42.71
 
< 0.1%
42.61
 
< 0.1%
42.51
 
< 0.1%
42.22
< 0.1%
422
< 0.1%

rh
Real number (ℝ)

HIGH CORRELATION

Distinct749
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.97298075
Minimum-200
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:50.431441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile15.5
Q133.8
median48.5
Q361.6
95-th percentile77.1
Maximum88.7
Range288.7
Interquartile range (IQR)27.8

Descriptive statistics

Standard deviation49.46867681
Coefficient of variation (CV)1.237552864
Kurtosis17.08049849
Mean39.97298075
Median Absolute Deviation (MAD)13.8
Skewness-4.05847051
Sum319703.9
Variance2447.149985
MonotonicityNot monotonic
2022-04-29T14:01:50.513514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
53.129
 
0.4%
57.926
 
0.3%
50.825
 
0.3%
61.125
 
0.3%
60.825
 
0.3%
57.624
 
0.3%
50.924
 
0.3%
50.124
 
0.3%
42.823
 
0.3%
Other values (739)7484
93.6%
ValueCountFrequency (%)
-200289
3.6%
9.22
 
< 0.1%
9.31
 
< 0.1%
9.61
 
< 0.1%
9.81
 
< 0.1%
9.91
 
< 0.1%
102
 
< 0.1%
10.21
 
< 0.1%
10.71
 
< 0.1%
10.91
 
< 0.1%
ValueCountFrequency (%)
88.71
 
< 0.1%
87.21
 
< 0.1%
87.11
 
< 0.1%
871
 
< 0.1%
86.61
 
< 0.1%
86.52
< 0.1%
861
 
< 0.1%
85.73
< 0.1%
85.61
 
< 0.1%
85.51
 
< 0.1%

ah
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5918
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.183808665
Minimum-200
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)3.6%
Memory size62.6 KiB
2022-04-29T14:01:50.602442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile0.350175
Q10.7793
median1.0316
Q31.358525
95-th percentile1.741645
Maximum2.231
Range202.231
Interquartile range (IQR)0.579225

Descriptive statistics

Standard deviation37.53100978
Coefficient of variation (CV)-6.069238525
Kurtosis22.72172521
Mean-6.183808665
Median Absolute Deviation (MAD)0.2867
Skewness-4.971215237
Sum-49458.1017
Variance1408.576695
MonotonicityNot monotonic
2022-04-29T14:01:50.693521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200289
 
3.6%
0.83946
 
0.1%
1.11996
 
0.1%
0.87365
 
0.1%
0.92715
 
0.1%
0.83255
 
0.1%
0.96845
 
0.1%
1.05945
 
0.1%
0.89444
 
0.1%
1.05514
 
0.1%
Other values (5908)7664
95.8%
ValueCountFrequency (%)
-200289
3.6%
0.19881
 
< 0.1%
0.20291
 
< 0.1%
0.2181
 
< 0.1%
0.21851
 
< 0.1%
0.21931
 
< 0.1%
0.23971
 
< 0.1%
0.2421
 
< 0.1%
0.24621
 
< 0.1%
0.24771
 
< 0.1%
ValueCountFrequency (%)
2.2311
< 0.1%
2.18061
< 0.1%
2.17661
< 0.1%
2.17191
< 0.1%
2.13951
< 0.1%
2.13621
< 0.1%
2.12471
< 0.1%
2.11951
< 0.1%
2.1171
< 0.1%
2.11641
< 0.1%

Interactions

2022-04-29T14:01:46.822024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.081895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.153097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.231096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.271096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.368027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.347096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.477115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.488106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.586025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.620077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.719025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.709094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.900023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.236029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.228117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.306094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.345099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.442026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.426027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.551025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.561025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.662027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.695024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.794025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.786094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.977023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.311097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.303027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.385027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.421027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.512026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.502027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.627095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.639119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.740024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.770025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.866026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.863105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.062026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.389097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.383028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.468026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.503026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.593101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.584029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.708039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.719096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.830096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.853096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.945096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.945102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.144094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.462027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.461097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.548027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.579102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.666027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.664027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.787110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.795110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.907107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.927026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.019025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.022024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.222067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.537097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.531096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.625096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.655026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.738026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.742026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.859096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.869107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.982026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.002025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.093025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.099061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.309112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.616036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.612027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.707108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.737095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.817027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.824027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.937025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.949038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.065025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.079096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.175095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.180101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.388025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.693098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.686026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.786026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.813100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.892026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.904025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.013098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.027024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.145095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.158024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.247104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.261093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.467148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.770103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.762027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.866028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.891097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.968026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.986097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.090128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.104024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.220095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.236025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.322024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.338094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.547138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.845107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.925097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.946047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.057103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.043096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.155097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.166025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.272026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.297095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.406035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.397025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.508023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.630140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.923098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.997026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.025096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.133069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.115096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.235042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.244025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.350024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.376098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.482024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.473096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.586024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.711140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:34.997026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.073106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.105124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.207107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.190102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.312026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.327026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.424096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.451082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.556095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.547093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.660094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:47.792138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:35.072027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:36.147099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:37.184026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:38.287102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:39.267027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:40.392065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:41.406105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:42.502040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:43.530102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:44.637025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:45.627094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-29T14:01:46.739105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-29T14:01:50.789798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-29T14:01:50.919897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-29T14:01:51.048968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-29T14:01:51.175897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-29T14:01:47.952145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-29T14:01:48.119214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
02004-03-10 18:00:002.6136015011.9104616610561131692126813.648.90.7578
12004-03-10 19:00:002.012921129.4955103117492155997213.347.70.7255
22004-03-10 20:00:002.21402889.093913111401141555107411.954.00.7502
32004-03-10 21:00:002.21376809.294817210921221584120311.060.00.7867
42004-03-10 22:00:001.61272516.583613112051161490111011.259.60.7888
52004-03-10 23:00:001.21197384.775089133796139394911.259.20.7848
62004-03-11 00:00:001.21185313.669062146277133373311.356.80.7603
72004-03-11 01:00:001.01136313.367262145376133373010.760.00.7702
82004-03-11 02:00:000.91094242.360945157960127662010.759.70.7648
92004-03-11 03:00:000.61010191.7561-2001705-200123550110.360.20.7517

Last rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
79882005-02-06 14:00:001.0868-2002.1590127108110075342010.626.00.3320
79892005-02-06 15:00:000.8868-2001.95769611287875536310.327.70.3481
79902005-02-06 16:00:001.0904-2002.7633138104010078941010.228.30.3516
79912005-02-06 17:00:001.4944-2003.76932179281508325689.229.90.3479
79922005-02-06 18:00:001.1925-2002.964918610031428195706.936.40.3635
79932005-02-06 19:00:001.6985-2004.57362278911658757746.038.00.3584
79942005-02-06 20:00:001.81002-2005.37802528551798928575.836.40.3385
79952005-02-06 21:00:001.4938-2003.76921939371498057375.835.40.3286
79962005-02-06 22:00:001.1896-2002.662715810331267826105.436.60.3304
79972005-02-06 23:00:001.0907-2002.461415010521207826275.137.90.3358